Digital Image Analysis for Personalized Travel Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional image analysis techniques focus on identifying locations or items within images but fail to derive travel-related insights or determine user experience from those items, lacking personalization and subjective information.
Innovation Solution
A system that correlates user data, such as transaction and image data, using computer vision and machine learning models to generate personalized trip recommendations by identifying travel features and employing facial emotion recognition technology to assess user interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional image analysis techniques are used to identify locations or items, then objective information can be obtained, but personal or subjective information specific to individual users cannot be gleaned
Solution Approach 1:
The patent combines traditional computer vision techniques for identifying travel features with machine learning models that process user data (transaction data, facial expressions, text) to extract personal information. This merging allows the system to maintain objective location identification while adding subjective user insight extraction, resolving the contradiction between obtaining objective information and gaining personalization capability.
Solution Approach 2:
The machine learning model acts as an intermediary between the image data and user profile generation. It processes multiple data types (visual, textual, transactional) to create personalized user representations, enabling the system to infer personal information without directly exposing raw data, thus maintaining information security while achieving personalization.
2Measurement precision
If multiple data types (transaction data, image data, facial expressions) are integrated to generate personalized recommendations, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the data processing into distinct modules: computer vision module for image analysis, natural language processing module for text analysis, transaction data processing module, and machine learning recommendation module. Each module handles specific data types independently before integrating results, reducing overall system complexity while maintaining high recommendation accuracy through specialized processing.
Solution Approach 2:
The machine learning model serves multiple functions: it processes different data types (images, text, transactions), generates user profiles, predicts preferences, and ranks recommendations. This multi-functionality reduces the need for separate specialized systems, achieving high accuracy without proportionally increasing complexity.
3Measurement precision
If machine learning models process large amounts of user data to create personalized profiles, then trip recommendation relevance improves, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user data in structured formats before recommendation requests are made. User profiles, travel feature databases, and historical data are prepared in advance, allowing the machine learning model to quickly query and process information during actual recommendation generation, thus reducing real-time processing time while maintaining high relevance.
Solution Approach 2:
The system applies partial action by selectively processing only the most relevant data types and features for each specific recommendation request rather than processing all available data comprehensively. This allows the model to achieve sufficient recommendation relevance without the time cost of complete data analysis, optimizing the balance between accuracy and speed.
Data Source
AI summary
Disclosed embodiments may include a system configured to perform digital image analysis. The system may receive transaction data and image data associated with a user. The system may identify, from the transaction data, first travel feature(s). The system may identify, from the image data via computer vision, second travel feature(s). The system may train a machine learning model (MLM) to generate trip recommendation(s) for the user based on the first travel feature(s) and the second travel feature(s). The system may determine, via the trained MLM, whether at least a first trip recommendation of the trip recommendation(s) exceeds a predetermined threshold indicating a likelihood the user will be interested in the first trip recommendation. Responsive to determining the first trip recommendation exceeds the predetermined threshold, the system may provide the first trip recommendation to the user.